Deriving Models for Software Project Effort Estimation By Means of Genetic Programming

Software engineering, effort estimation, genetic programming, symbolic regression. This paper presents the application of a computational intelligence methodology in effort estimation for software projects. Namely, we apply a genetic programming model for symbolic regression; aiming to produce mathematical expressions that (1) are highly accurate and (2) can be used for estimating the development effort by revealing relationships between the project’s features and the required work. We selected to investigate the effectiveness of this methodology into two software engineering domains. The system was proved able to generate models in the form of handy mathematical expressions that are more accurate than those found in literature.

An approach to software cost estimation

A general procedure for software cost estimation in any environment is outlined. The basic concepts of work and effort estimation are explained, some popular resource estimation models are reviewed, and the accuracy of source estimates is discussed. A software cost prediction procedure based on the experiences of the Software Engineering Laboratory in the flight dynamics area and incorporating management expertise, cost models, and historical data is described. The sources of information and relevant parameters available during each phase of the software life cycle are identified. The methodology suggested incorporates these elements into a customized management tool for software cost prediction. Detailed guidelines for estimation in the flight dynamics environment developed using this methodology are presented

Investigating the mass of the intermediate mass black hole candidate HLX-1 with the SLIMBH model

In this paper we present a comprehensive study of the mass of the intermediate mass black hole candidate HLX-1 in the galaxy ESO 243-49. We analyse the continuum X-ray spectra collected by Swift, XMM-Newton, and Chandra with the slim disc model, SLIMBH, and estimate the black hole mass for the full range of inclination (inc = 0{\deg} - 85{\deg}) and spin (a* = 0 - 0.998). The relativistic SLIMBH model is particularly suited to study high luminosity disc spectra as it incorporates the effects of advection, such as the shift of the inner disc edge towards smaller radii and the increasing height of the disc photosphere (including relativistic ray-tracing from its proper location rather than the mid-plane of the disc). We find for increasing values of inclination that a zero spin black hole has a mass range of 6,300 - 50,900 M_sun and a maximally spinning black hole has a mass between 16,900 - 191,700 M_sun. This is consistent with previous estimates and reinforces the idea that HLX-1 contains an intermediate mass black hole.Comment: updated version, published in Astronomy and Astrophysic

Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas

We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism

Radio Galaxy Detection in the Visibility Domain

We explore a new Bayesian method of detecting galaxies from radio interferometric data of the faint sky. Working in the Fourier domain, we fit a single, parameterised galaxy model to simulated visibility data of star-forming galaxies. The resulting multimodal posterior distribution is then sampled using a multimodal nested sampling algorithm such as MultiNest. For each galaxy, we construct parameter estimates for the position, flux, scale-length and ellipticities from the posterior samples. We first test our approach on simulated SKA1-MID visibility data of up to 100 galaxies in the field of view, considering a typical weak lensing survey regime (SNR $\ge 10$) where 98% of the input galaxies are detected with no spurious source detections. We then explore the low SNR regime, finding our approach reliable in galaxy detection and providing in particular high accuracy in positional estimates down to SNR $\sim 5$. The presented method does not require transformation of visibilities to the image domain, and requires no prior knowledge of the number of galaxies in the field of view, thus could become a useful tool for constructing accurate radio galaxy catalogs in the future.Comment: 11 pages, 11 figures. Accepted for publication in MNRA